Research on a nondestructive model for the detection of the nitrogen content of tomato

Abstract

The timely detection of information on crop nutrition is of great significance for improving the production efficiency of facility crops. In this study, the terahertz (THz) spectral information of tomato plant leaves with different nitrogen levels was obtained. The noise reduction of the THz spectral data was then carried out by using the Savitzky-Golay (S-G) smoothing algorithm. The sample sets were then analyzed by using Kennard-Stone (KS) and random sampling (RS) methods, respectively. The KS algorithm was optimized to divide the sample sets. The stability competitive adaptive reweighted sampling (SCARS), uninformative variable elimination (UVE), and interval partial least-squares (iPLS) algorithms were then used to screen the pre-processed THz spectral data. Based on the selected characteristic frequency bands, a model for the detection of the nitrogen content of tomato based on the THz spectrum was established by the radial basis function neural network (RBFNN) and backpropagation neural network (BPNN) algorithms, respectively. The results show that the root-mean-square error of correction (RMSEC) and root-mean-square error of prediction (RMSEP) of the BPNN model were respectively 0.1722% and 0.1843%, and the determination coefficients of the correction set (Rc2) and prediction set (Rp2) were respectively 0.8447 and 0.8375. The RMSEC and RMSEP values of the RBFNN model were respectively 0.1322% and 0.1855%, and the Rc2 and Rp2 values were respectively 0.8714 and 0.8463. Thus, the accuracy of the model established by the RBFNN algorithm was slightly higher. Therefore, the nitrogen content of tomato leaves can be detected by THz spectroscopy. The results of this study can provide a theoretical basis for the research and development of equipment for the detection of the nitrogen content of tomato leaves.

Assessing Fermentation Broth Quality of Pineapple Vinegar Production with a Near-Infrared Fiber-Optic Probe Coupled with Stability Competitive Adaptive Reweighted Sampling

“…SCARS is a wavelength selection method, first described by Zheng et al [21], that aims to select important NIR spectral variables to improve the performance of PLS calibration models. The capabilities of the SCARS method have been assessed in numerous studies under different conditions and environments since it was established [44][45][46].…”

The Prediction Model of Total Nitrogen Content in Leaves of Korla Fragrant Pear Was Established Based on Near Infrared Spectroscopy

“…Zhang et al [16] employed terahertz spectral data in conjunction with radial basis function neural network (RBFNN) and back-propagation neural network (BPNN) algorithms to develop a model for detecting tomato nitrogen content based on terahertz spectra under nitrogen stress conditions. The researchers gathered 80 samples from 65-day-old tomato plants, selecting 20 leaf samples for each of the 4 nitrogen stress gradients.…”

Detection of tomato water stress based on terahertz spectroscopy

“…Tomatoes require a long growth period and a large amount of water, and because of their extreme sensitivity to water, they have specific requirements for water at different growth stages. Insufficient water inhibits the absorption of nutrients by crops and slows the growth of leaf area, plant height and stem thickness, resulting in reduced crop quality and yield ( Zhang et al., 2023 ). Therefore, the rapid, accurate and non-destructive detection of tomato water status is very important for scientifically and effectively managing tomato water and fertilizer use, improving water resource utilization efficiency, and ensuring tomato yield and quality in China.…”